Int. J. Modelling, Identification and Control, Vol. 23, No. 1, 2015 85 Copyright © 2015 Inderscience Enterprises Ltd. A parsimonious friction model for efficient identification and compensation of hysteresis with non-local memory Alessio Merola*, Domenico Colacino, Carlo Cosentino and Francesco Amato School of Computer and Biomedical Engineering, Università degli Studi Magna Græcia di Catanzaro, Campus Universitario di Germaneto, 88100 Catanzaro, Italy Email: merola@unicz.it Email: colacino@unicz.it Email: carlo.cosentino@unicz.it Email: amato@unicz.it *Corresponding author Abstract: A novel dynamic friction model, which allows to capture friction hysteresis with non-local memory, is presented in this paper. The model is conceived in order to find a trade-off between accuracy of the model prediction and difficulty of implementation in motion control systems with model-based friction compensation. The hysteresis function introduced into the model accounts for non-local memory, i.e., the property for which the friction output depends not only on the initial conditions but also on past extremum values of the input or the output. In comparison with other models incorporating a hysteresis function with non-local memory, the proposed model is demonstrated to reduce the number of parameters necessary to reproduce the hysteresis loops observed experimentally. Moreover, parameter identification can benefit from the availability of a closed form of the model solution. Keywords: friction modelling; hysteresis with non-local memory; identification of friction dynamics; friction compensation; presliding regime Reference to this paper should be made as follows: Merola, A., Colacino, D., Cosentino, C. and Amato, F. (2015) ‘A parsimonious friction model for efficient identification and compensation of hysteresis with non-local memory’, Int. J. Modelling, Identification and Control, Vol. 23, No. 1, pp.85–91. Biographical notes: Alessio Merola is an Assistant Professor in Systems and Control Engineering in the School of Computer and Biomedical Engineering at Magna Græcia University of Catanzaro, Italy. His research interests include analysis and control of nonlinear and uncertain systems, modelling, identification and control of (bio-)mechatronic systems, and systems biology. He is a member of the IEEE Control Systems Society and the IEEE/IES Technical Committee on Motion Control. Domenico Colacino is a Postdoctoral at the Department of Experimental and Clinical Medicine, Magna Græcia University of Catanzaro, Italy. He received his PhD in Biomedical and Computer Engineering from Magna Græcia University of Catanzaro in 2014. His research interests include analysis and control of nonlinear and uncertain systems, modelling, identification and control of (bio-)mechatronic systems. Carlo Cosentino is an Assistant Professor in Systems and Control Engineering in the School of Computer and Biomedical Engineering at Magna Græcia University of Catanzaro, Italy. His current research interests are in the field of systems and control theory, biomechatronics and systems biology. Francesco Amato is a Full Professor of Bioengineering, the Dean of the School of Computer and Biomedical Engineering, the Coordinator of the Doctorate School in Biomedical and Computer Engineering and the Director of the Biomechatronics Laboratory at Magna Græcia University of Catanzaro, Italy. His main research interests concern analysis and control of uncertain systems, finite-time stability of linear systems, and, more recently, stability analysis of nonlinear quadratic systems.